Object Classification. The labels provided by the Taskonomy dataset [1]} are activations generated by a ResNet-152 model [2]} pre-trained on ImageNet [3]}. For object classification, we train networks with the provided activations. Since we use a subset of the Taskonomy dataset, we identified 75 classes of objects that appear in our selected subset for network training. The data augmentations applied for this task are random flip, color jittering, and normalization. For each network, the decoder part contains a Global Average Pooling (GAP) layer and a linear layer. Referring to the settings of Taskonomy, each network is trained for 25 epochs. Throughout the learning process, we use a cosine annealing scheduler to gradually reduce the learning rate from 0.1 to 0 for fast convergence. The optimizer for parameters is SGD with the momentum factor 0.9, 0.0005 weight decay, and Nesterov momentum is enabled.
